Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Toward 5G densenets: architectural advances for effective machine-type communications over femtocells

Abstract: Ubiquitous, reliable and low-latency machinetype communication, MTC, systems are considered to be value-adds of emerging 5G cellular networks. To meet the technical and economical requirements for exponentially growing MTC traffic, we advocate the use of small cells to handle the massive and dense MTC rollout. We introduce a novel 3GPP-compliant architecture that absorbs the MTC traffic via home evolved NodeBs, allowing us to significantly reduce congestion and overloading of radio access and core networks. A major design challenge has been to deal with the interference to human-type traffic and the large degree of freedom of the system, due to the unplanned deployments of small cells and the enormous amount of MTC devices. Simulation results in terms of MTC access delay, energy consumption, and delivery rate corroborate the superiority of the proposed working architecture.

Self-Supervised Convolutional Subspace Clustering Network

Abstract: Subspace clustering methods based on data self-expression have become very popular for learning from data that lie in a union of low-dimensional linear subspaces. However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces. On the other hand, while Convolutional Neural Network (ConvNet) has been demonstrated to be a powerful tool for extracting discriminative features from visual data, training such a ConvNet usually requires a large amount of labeled data, which are unavailable in subspace clustering applications. To achieve simultaneous feature learning and subspace clustering, we propose an end-to-end trainable framework, called Self-Supervised Convolutional Subspace Clustering Network (S$^2$ConvSCN), that combines a ConvNet module (for feature learning), a self-expression module (for subspace clustering) and a spectral clustering module (for self-supervision) into a joint optimization framework. Particularly, we introduce a dual self-supervision that exploits the output of spectral clustering to supervise the training of the feature learning module (via a classification loss) and the self-expression module (via a spectral clustering loss). Our experiments on four benchmark datasets show the effectiveness of the dual self-supervision and demonstrate superior performance of our proposed approach.

Delay-Optimal Virtualized Radio Resource Scheduling in Software-Defined Vehicular Networks via Stochastic Learning

Abstract: Due to the high density of vehicles and various types of vehicular services, it is challenging to guarantee the quality of vehicular services in current Long-Term Evolution (LTE) networks in a cost-efficient manner. Fortunately, with the development of fifth-generation (5G) technology, the installation of a large number of small cells is foreseen as one of the practical ways to achieve the low-delay requirement in vehicular environments. However, it may cause a huge operating expense and capital expenditure to mobile network operators due to the limited backhaul capacity and the explosion of signaling. In this paper, we integrate software-defined networking and radio resource virtualization into an LTE system for vehicular networks, i.e., software-defined heterogeneous vehicular network (SERVICE) . Based on this proposed system framework, a delay-optimal virtualized radio resource scheduling scheme is proposed via stochastic learning. The delay optimal problem is formulated as an infinite-horizon average-cost partially observed Markov decision process (POMDP). Then, an equivalent Bellman equation is derived to solve it. The proposed scheme can be divided into two stages, i.e., macro virtualization resource allocation (MaVRA) and micro virtualization resource allocation (MiVRA). The former is executed based on large timescale variables (traffic density), whereas the latter is operated according to short timescale variables (channel state and queue state). Simulation results show that the proposed scheme outperforms traditional schemes.

Revisiting the security of quantum dialogue and bidirectional quantum secure direct communication

Abstract: From the perspective of information theory and cryptography, the security of two quantum dialogue protocols and a bidirectional quantum secure direct communication (QSDC) protocol was analyzed, and it was pointed out that the transmitted information would be partly leaked out in them. That is, any eavesdropper can elicit some information about the secrets from the public annunciations of the legal users. This phenomenon should have been strictly forbidden in a quantum secure communication. In fact, this problem exists in quite a few recent proposals and, therefore, it deserves more research attention in the following related study.